{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ccc7a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def calXnew(Xold):\n",
    "    return(1/Xold)+(Xold/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea2728cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f122dc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i: 0  Xold: 10  Xnew: 5.1  Diff: 4.9\n",
      "i: 1  Xold: 5.1  Xnew: 2.746078431372549  Diff: 2.3539215686274506\n",
      "i: 2  Xold: 2.746078431372549  Xnew: 1.7371948743795984  Diff: 1.0088835569929506\n",
      "i: 3  Xold: 1.7371948743795984  Xnew: 1.4442380948662321  Diff: 0.2929567795133663\n",
      "i: 4  Xold: 1.4442380948662321  Xnew: 1.4145256551487377  Diff: 0.029712439717494377\n",
      "i: 5  Xold: 1.4145256551487377  Xnew: 1.4142135968022693  Diff: 0.00031205834646841346\n",
      "i: 6  Xold: 1.4142135968022693  Xnew: 1.4142135623730954  Diff: 3.442917395624079e-08\n",
      "finished the result is  1.4142135623730954\n"
     ]
    }
   ],
   "source": [
    "Xold = 10\n",
    "error = 1e-4\n",
    "i = 0\n",
    "maxIter = 50\n",
    "listXnew = []\n",
    "listXold= []\n",
    "list_i = []\n",
    "listdiff = []\n",
    "\n",
    "while(i < maxIter):\n",
    "    Xnew = calXnew(Xold)\n",
    "    difference = abs(Xnew - Xold)\n",
    "    listXnew.append(Xnew)    \n",
    "    list_i.append(i)\n",
    "    listdiff.append(difference)\n",
    "    listXold.append(Xold)\n",
    "    \n",
    "    print(\"i:\",i,\" Xold:\",Xold,\" Xnew:\",Xnew,\" Diff:\",difference)\n",
    "    if difference < error:\n",
    "        print(\"finished the result is \",Xnew)\n",
    "        break  \n",
    "             \n",
    "    Xold = Xnew\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77813c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Xnew')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list_i,listXnew)\n",
    "plt.xlabel(\"i\",fontsize = 18)\n",
    "plt.ylabel(\"Xnew\",fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3849091b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Xnew')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(listXold,listXnew)\n",
    "plt.xlabel(\"Xold\",fontsize = 18)\n",
    "plt.ylabel(\"Xnew\",fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8221af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Difference')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list_i,listdiff)\n",
    "plt.xlabel(\"i\",fontsize = 18)\n",
    "plt.ylabel(\"Difference\",fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3b41ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
